Tag Archives: manufacturing

John Markoff and Matthew Rosenberg have written a fascinating analysis of the competition between US and China regarding technological advances, specifically in the field of artificial intelligence. While the focus of the Feb. 3, 2017 NY Times article is military, the authors make it easy to extrapolate and apply the concepts to other sectors,

Robert O. Work, the veteran defense official retained as deputy secretary by President Trump, calls them his “A.I. dudes.” The breezy moniker belies their serious task: The dudes have been a kitchen cabinet of sorts, and have advised Mr. Work as he has sought to reshape warfare by bringing artificial intelligence to the battlefield.

Last spring, he asked, “O.K., you guys are the smartest guys in A.I., right?”

No, the dudes told him, “the smartest guys are at Facebook and Google,” Mr. Work recalled in an interview.

Now, increasingly, they’re also in China. The United States no longer has a strategic monopoly on the technology, which is widely seen as the key factor in the next generation of warfare.

The Pentagon’s plan to bring A.I. to the military is taking shape as Chinese researchers assert themselves in the nascent technology field. And that shift is reflected in surprising commercial advances in artificial intelligence among Chinese companies. [emphasis mine]

Having read Marshal McLuhan (de rigeur for any Canadian pursuing a degree in communications [sociology-based] anytime from the 1960s into the late 1980s [at least]), I took the movement of technology from military research to consumer applications as a standard. Television is a classic example but there are many others including modern plastic surgery. The first time, I encountered the reverse (consumer-based technology being adopted by the military) was in a 2004 exhibition “Massive Change: The Future of Global Design” produced by Bruce Mau for the Vancouver (Canada) Art Gallery.

Markoff and Rosenberg develop their thesis further (Note: Links have been removed),

Last year, for example, Microsoft researchers proclaimed that the company had created software capable of matching human skills in understanding speech.

Although they boasted that they had outperformed their United States competitors, a well-known A.I. researcher who leads a Silicon Valley laboratory for the Chinese web services company Baidu gently taunted Microsoft, noting that Baidu had achieved similar accuracy with the Chinese language two years earlier.

That, in a nutshell, is the challenge the United States faces as it embarks on a new military strategy founded on the assumption of its continued superiority in technologies such as robotics and artificial intelligence.

First announced last year by Ashton B. Carter, President Barack Obama’s defense secretary, the “Third Offset” strategy provides a formula for maintaining a military advantage in the face of a renewed rivalry with China and Russia.

…

As consumer electronics manufacturing has moved to Asia, both Chinese companies and the nation’s government laboratories are making major investments in artificial intelligence.

The advance of the Chinese was underscored last month when Qi Lu, a veteran Microsoft artificial intelligence specialist, left the company to become chief operating officer at Baidu, where he will oversee the company’s ambitious plan to become a global leader in A.I.

The authors note some recent military moves (Note: Links have been removed),

In August [2016], the state-run China Daily reported that the country had embarked on the development of a cruise missile system with a “high level” of artificial intelligence. The new system appears to be a response to a missile the United States Navy is expected to deploy in 2018 to counter growing Chinese military influence in the Pacific.

Known as the Long Range Anti-Ship Missile, or L.R.A.S.M., it is described as a “semiautonomous” weapon. According to the Pentagon, this means that though targets are chosen by human soldiers, the missile uses artificial intelligence technology to avoid defenses and make final targeting decisions.

The new Chinese weapon typifies a strategy known as “remote warfare,” said John Arquilla, a military strategist at the Naval Post Graduate School in Monterey, Calif. The idea is to build large fleets of small ships that deploy missiles, to attack an enemy with larger ships, like aircraft carriers.

“They are making their machines more creative,” he said. “A little bit of automation gives the machines a tremendous boost.”

Whether or not the Chinese will quickly catch the United States in artificial intelligence and robotics technologies is a matter of intense discussion and disagreement in the United States.

Markoff and Rosenberg return to the world of consumer electronics as they finish their article on AI and the military (Note: Links have been removed),

Moreover, while there appear to be relatively cozy relationships between the Chinese government and commercial technology efforts, the same cannot be said about the United States. The Pentagon recently restarted its beachhead in Silicon Valley, known as the Defense Innovation Unit Experimental facility, or DIUx. It is an attempt to rethink bureaucratic United States government contracting practices in terms of the faster and more fluid style of Silicon Valley.

The government has not yet undone the damage to its relationship with the Valley brought about by Edward J. Snowden’s revelations about the National Security Agency’s surveillance practices. Many Silicon Valley firms remain hesitant to be seen as working too closely with the Pentagon out of fear of losing access to China’s market.

“There are smaller companies, the companies who sort of decided that they’re going to be in the defense business, like a Palantir,” said Peter W. Singer, an expert in the future of war at New America, a think tank in Washington, referring to the Palo Alto, Calif., start-up founded in part by the venture capitalist Peter Thiel. “But if you’re thinking about the big, iconic tech companies, they can’t become defense contractors and still expect to get access to the Chinese market.”

Those concerns are real for Silicon Valley.

If you have the time, I recommend reading the article in its entirety.

Impact of the US regime on thinking about AI?

A March 24, 2017 article by Daniel Gross for Slate.com hints that at least one high level offician in the Trump administration may be a little naïve in his understanding of AI and its impending impact on US society (Note: Links have been removed),

Treasury Secretary Steven Mnuchin is a sharp guy. He’s a (legacy) alumnus of Yale and Goldman Sachs, did well on Wall Street, and was a successful movie producer and bank investor. He’s good at, and willing to, put other people’s money at risk alongside some of his own. While he isn’t the least qualified person to hold the post of treasury secretary in 2017, he’s far from the best qualified. For in his 54 years on this planet, he hasn’t expressed or displayed much interest in economic policy, or in grappling with the big picture macroeconomic issues that are affecting our world. It’s not that he is intellectually incapable of grasping them; they just haven’t been in his orbit.

Which accounts for the inanity he uttered at an Axios breakfast Friday morning about the impact of artificial intelligence on jobs.

“it’s not even on our radar screen…. 50-100 more years” away, he said. “I’m not worried at all” about robots displacing humans in the near future, he said, adding: “In fact I’m optimistic.”

…

A.I. is already affecting the way people work, and the work they do. (In fact, I’ve long suspected that Mike Allen, Mnuchin’s Axios interlocutor, is powered by A.I.) I doubt Mnuchin has spent much time in factories, for example. But if he did, he’d see that machines and software are increasingly doing the work that people used to do. They’re not just moving goods through an assembly line, they’re soldering, coating, packaging, and checking for quality. Whether you’re visiting a GE turbine plant in South Carolina, or a cable-modem factory in Shanghai, the thing you’ll notice is just how few people there actually are. It’s why, in the U.S., manufacturing output rises every year while manufacturing employment is essentially stagnant. It’s why it is becoming conventional wisdom that automation is destroying more manufacturing jobs than trade. And now we are seeing the prospect of dark factories, which can run without lights because there are no people in them, are starting to become a reality. The integration of A.I. into factories is one of the reasons Trump’s promise to bring back manufacturing employment is absurd. You’d think his treasury secretary would know something about that.

It goes far beyond manufacturing, of course. Programmatic advertising buying, Spotify’s recommendation engines, chatbots on customer service websites, Uber’s dispatching system—all of these are examples of A.I. doing the work that people used to do. …

…

Adding to Mnuchin’s lack of credibility on the topic of jobs and robots/AI, Matthew Rozsa’s March 28, 2017 article for Salon.com features a study from the US National Bureau of Economic Research (Note: Links have been removed),

A new study by the National Bureau of Economic Research shows that every fully autonomous robot added to an American factory has reduced employment by an average of 6.2 workers, according to a report by BuzzFeed. The study also found that for every fully autonomous robot per thousand workers, the employment rate dropped by 0.18 to 0.34 percentage points and wages fell by 0.25 to 0.5 percentage points.

I can’t help wondering if the US Secretary of the Treasury is so oblivious to what is going on in the workplace whether that’s representative of other top-tier officials such as the Secretary of Defense, Secretary of Labor, etc. What is going to happen to US research in fields such as robotics and AI?

I have two more questions, in future what happens to research which contradicts or makes a top tier Trump government official look foolish? Will it be suppressed?

You can find the report “Robots and Jobs: Evidence from US Labor Markets” by Daron Acemoglu and Pascual Restrepo. NBER (US National Bureau of Economic Research) WORKING PAPER SERIES (Working Paper 23285) released March 2017 here. The introduction featured some new information for me; the term ‘technological unemployment’ was introduced in 1930 by John Maynard Keynes.

Moving from a wholly US-centric view of AI

Naturally in a discussion about AI, it’s all US and the country considered its chief sceince rival, China, with a mention of its old rival, Russia. Europe did rate a mention, albeit as a totality. Having recently found out that Canadians were pioneers in a very important aspect of AI, machine-learning, I feel obliged to mention it. You can find more about Canadian AI efforts in my March 24, 2017 posting (scroll down about 40% of the way) where you’ll find a very brief history and mention of the funding for a newly launching, Pan-Canadian Artificial Intelligence Strategy.

If any of my readers have information about AI research efforts in other parts of the world, please feel free to write them up in the comments.

Sept. 30, 2013 marks the date for the launch of Singapore’s Nanoimprint Foundry. From the Sept. 30, 2013 news item on Nanowerk,

A*STAR’s [Agency for Science, Technology and Research] Institute of Materials Research and Engineering (IMRE) and its partners launched a new Nanoimprint Foundry that will develop, test-bed and prototype specially engineered plastics and surfaces for the specific purpose of commercialising the technologies. Possible applications of nanoimprint technology include dry adhesives, aesthetic packaging, contact lenses, biomedical cell scaffolds, anti-frost surfaces and anti-bacteria materials.

The multi-party investment will bring together national research organisations, suppliers and manufacturers spanning the nanotechnology value chain, and government agencies to promote the technology. The Foundry is part of a masterplan spearheaded by A*STAR to push translational research and accelerate commercialisation of home-grown technologies. In partnership with other A*STAR research institutes, IMRE will work with companies like Toshiba Machines Co Ltd, EV Group, NTT Advanced Technology Corporation, NIL Technology ApS, Kyodo International Inc., micro resist technology GmbH, Nanoveu Pte Ltd and Solves Innovative Technology Pte Ltd to produce prototypes for real-world products and applications. The Foundry and its partners will also work closely with Singapore’s Economic Development Board (EDB) and SPRING to promote its nanoimprint applications to industry as part of the plans to build up Singapore’s high-value manufacturing capabilities.

3. “We can help companies develop up to 20,000 samples for proof-of-concept and pilot production allowing manufacturers to shorten the product cycle but minus the heavy capital R&D investment”, said Dr Karen Chong, the IMRE scientist who is heading the Foundry. Dr Chong added that the Foundry will be a one-stop shop for companies seeking to conceive, design and develop solutions for new, revolutionary products based on the versatile nanoimprint technology.

4. “The Foundry gives us the tools for creating real products that target industry end users and ultimately consumers”, explained Mr Masayuki Yagi, Director & General Manager, Advanced Machinery Business Unit, Toshiba Machines Co Ltd, Japan on why the company chose to participate in the initiative. “Toshiba Machines and the Foundry will aim to deliver innovative engineering solutions based on nanoimprint and be the best partner for leading industries”.

5. According to Mr Koh Teng Kwee, Director of Solves Innovative Technology Pte Ltd, “Working with IMRE since IICON 1[1] am sure IMRE’s nanoimprint technology and know-how is now ready for industrial adoption. In my opinion, IMRE is able to provide everything needed for a new product realisation involving nanoimprinting.”

6. “There is a billion-dollar, virtually untapped market for new advanced nanotechnology products that can make use of what the Foundry has to offer”, said Prof Andy Hor, Executive Director for IMRE, adding that the initiative will hasten the industrialisation of nanoimprinting in this lucrative market segment. In consumer care for example, the global market for contact lenses – where nanoimprint technology can be used to produce new functionalities like multi-coloured lenses – is expected to grow to USD 11.7 billion by 2015[2].

7. “The Foundry is the first one-stop shop to pull different value chain partners together to offer solutions based on nanoimprint through equipment, moulds, materials and applications to end user companies”, said Dr Tan Geok Leng, Executive Director of A*STAR’s Science and Engineering Research Council which oversees a number of the research institutes dedicated to the physical sciences and engineering. “The new Foundry is part of Singapore’s strategy to create a new, advanced high-value manufacturing sector to support its growing knowledge-based economy.”

8. “As part of EDB’s vision to position Singapore as an Advanced Manufacturing Hub, we will continue to work with companies to co-create and adopt advanced manufacturing technologies. We see this new Research Foundry as one of the key infrastructures to strengthen nanoscale-manufacturing capabilities in Singapore”, said Mr Yi-Hsen Gian, Director (i3), Economic Development Board (EDB), Singapore.

With all the talk about self-assembling DNA nanotechnology, it’s possible to misunderstand the stage of development this endeavour occupies as the title, Reality check for DNA Nanotechnology, for a Dec. 13, 2012 news release on EurekAlert suggests,

… This emerging technology employs DNA as a programmable building material for self-assembled, nanometer-scale structures. Many practical applications have been envisioned, and researchers recently demonstrated a synthetic membrane channel made from DNA. Until now, however, design processes were hobbled by a lack of structural feedback. Assembly was slow and often of poor quality.

In fact, the news release is touting two breakthroughs,

Now researchers led by Prof. Hendrik Dietz of the Technische Universitaet Muenchen (TUM) have removed these obstacles.

One barrier holding the field back was an unproven assumption. Researchers were able to design a wide variety of discrete objects and specify exactly how DNA strands should zip together and fold into the desired shapes. They could show that the resulting nanostructures closely matched the designs. Still lacking, though, was the validation of the assumed subnanometer-scale precise positional control. This has been confirmed for the first time through analysis of a test object designed specifically for the purpose. A technical breakthrough based on advances in fundamental understanding, this demonstration has provided a crucial reality check for DNA nanotechnology.

In a separate set of experiments, the researchers discovered that the time it takes to make a batch of complex DNA-based objects can be cut from a week to a matter of minutes, and that the yield can be nearly 100%. They showed for the first time that at a constant temperature, hundreds of DNA strands can fold cooperatively to form an object — correctly, as designed — within minutes. Surprisingly, they say, the process is similar to protein folding, despite significant chemical and structural differences. “Seeing this combination of rapid folding and high yield,” Dietz says, “we have a stronger sense than ever that DNA nanotechnology could lead to a new kind of manufacturing, with a commercial, even industrial future.” And there are immediate benefits, he adds: “Now we don’t have to wait a week for feedback on an experimental design, and multi-step assembly processes have suddenly become so much more practical.”

Dexter Johnson comments in his Dec. 18, 2012 posting (which includes an embedded video) on the Nanoclast blog (located on the Institute of Electrical and Electronics Engineers [IEEE] website),

The field of atomically precise manufacturing—or molecular manufacturing—has taken a big step towards realizing its promise with this latest research. We may still be a long way from realizing the “nanotech rapture” but certainly knowing that the objects built meet their design specifications and can be produced in minutes rather than weeks has to be recognized as a significant development.

Three papers have been published on these breakthroughs, here are the citations,

Here’s an intriguing approach to self-assembly for manufacturing purposes from scientists at Brown and Johns Hopkins Universities, respectively. From the Dec. 7, 2011 news item on Nanowerk,

In a paper published in the Proceedings of National Academy of Sciences (“Algorithmic design of self-folding polyhedra”), researchers from Brown and Johns Hopkins University determined the best 2-D arrangements, called planar nets, to create self-folding polyhedra with dimensions of a few hundred microns, the size of a small dust particle. The strength of the analysis lies in the combination of theory and experiment. The team at Brown devised algorithms to cut through the myriad possibilities and identify the best planar nets to yield the self-folding 3-D structures. Researchers at Johns Hopkins then confirmed the nets’ design principles with experiments.

Here’s the magnitude of the problem these scientists were solving (from the news item),

Material chemists and engineers would love to figure out how to create self-assembling shells, containers or structures that could be used as tiny drug-carrying containers or to build 3-D sensors and electronic devices.

There have been some successes with simple 3-D shapes such as cubes, but the list of possible starting points that could yield the ideal self-assembly for more complex geometric configurations gets long fast. For example, while there are 11 2-D arrangements for a cube, there are 43,380 for a dodecahedron (12 equal pentagonal faces). Creating a truncated octahedron (14 total faces – six squares and eight hexagons) has 2.3 million possibilities.

“The issue is that one runs into a combinatorial explosion. … How do we search efficiently for the best solution within such a large dataset? This is where math can contribute to the problem.”

Here’s how they solved the problem (from the news item),

“Using a combination of theory and experiments, we uncovered design principles for optimum nets which self-assemble with high yields,” said David Gracias, associate professor in of chemical and biomolecular engineering at Johns Hopkins and a co-corresponding author on the paper.

“In doing so, we uncovered striking geometric analogies between natural assembly of proteins and viruses and these polyhedra, which could provide insight into naturally occurring self-assembling processes and is a step toward the development of self-assembly as a viable manufacturing paradigm.”

“This is about creating basic tools in nanotechnology,” said Menon, co-corresponding author on the paper. “It’s important to explore what shapes you can build. The bigger your toolbox, the better off you are.” While the approach has been used elsewhere to create smaller particles at the nanoscale, the researchers at Brown and Johns Hopkins used larger sizes to better understand the principles that govern self-folding polyhedra.

The news item on Nanowerk features more details, a video of a self-assembling dodecahedron, and an image of various options for 2-D nets that can be used to create 3-D shapes.

“Using a combination of theory and experiments, we uncovered design principles for optimum nets which self-assemble with high yields,” said David Gracias, associate professor in of chemical and biomolecular engineering at Johns Hopkins and a co-corresponding author on the paper. “In doing so, we uncovered striking geometric analogies between natural assembly of proteins and viruses and these polyhedra, which could provide insight into naturally occurring self-assembling processes and is a step toward the development of self-assembly as a viable manufacturing paradigm.”

“This is about creating basic tools in nanotechnology,” said Menon, co-corresponding author on the paper. “It’s important to explore what shapes you can build. The bigger your toolbox, the better off you are.”

While the approach has been used elsewhere to create smaller particles at the nanoscale, the researchers at Brown and Johns Hopkins used larger sizes to better understand the principles that govern self-folding polyhedra.